{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Softmax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Download the CIFAR10 datasets, and load it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have two ways to download the CIFAR10 datasets:\n",
    "- cd the path to './CIFAR10/datasets/', and run the 'get_datasets.sh'. Then it will automatically download the datasets and decompress it.\n",
    "- Enter the CIFAR10 website: http://www.cs.toronto.edu/~kriz/cifar.html and manually download the python version. Then put the datasets in local folder './CIFAR10/datasets/'.\n",
    "\n",
    "The CIFAR10 datasets named 'cifar-10-batches-py/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from CIFAR10.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the CIFAR10 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'CIFAR10/datasets/cifar-10-batches-py'\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show some CIFAR10 images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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42qCeC74r177n4g2uQ46r1wOfG3BMb65YjIaechwHV5/jeh5e75muQ86XfvQ8L3uv67q4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Xtq+h1TSLT5xNwQIaWzOBBfXHZquBxdRQRV4Y4DLRH2maYrBp1JzxYmZ/L+fchls3GyE2Oqa/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Dly5MjxCPhCPVOlQtFUtU4dCddj1/90igMu9yLEVWxwoujG5hpefJFuqNeu3sDHH1OtrHqj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\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GI3YHSXNcv8EBKYMEFQ4egYLNCWUMlZ2h52S2s4QwOD6mdt2+dYxP/eYn6dWMxt4uzZ3j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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3328b64a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes = ['plane', 'car', 'bird', 'cat', 'dear', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "num_each_class = 7\n",
    "\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, num_each_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + (y + 1)\n",
    "        plt.subplot(num_each_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subsample the data for more efficient code execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n",
      "Development　data shape:  (500, 32, 32, 3)\n",
      "Development labels shape:  (500,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, test sets and dev sets\n",
    "num_train = 49000\n",
    "num_val = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Validation set\n",
    "mask = range(num_train, num_train + num_val)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Train set\n",
    "mask = range(num_train)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# Test set\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "# Development set\n",
    "mask = np.random.choice(num_train, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)\n",
    "print('Development　data shape: ', X_dev.shape)\n",
    "print('Development labels shape: ', y_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reshape the images data into rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "Development data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the images data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Development data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subtract the mean images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/wB9ExOcZLce9XdLVwO3Avoi4HNjXfG9mp4mxyR8j/918e2bzL4AbgL3N9r3Ajb1EaGa9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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd32f611da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Processing: subtract the mean images\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Append the bias dimension of ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 3073)\n",
      "Validation data shape:  (1000, 3073)\n",
      "Test data shape:  (1000, 3073)\n",
      "Development data shape:  (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# append the bias dimension of ones (i.e. bias trick)\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Development data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Define a linear Softmax classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Softmax(object):\n",
    "    def __init__(self):\n",
    "        self.W = None\n",
    "    \n",
    "    def loss_naive(self, X, y, reg):\n",
    "        \"\"\"\n",
    "        Structured Softmax loss function, naive implementation (with loops).\n",
    "        Inputs:\n",
    "        - X: A numpy array of shape (num_train, D) contain the training data\n",
    "          consisting of num_train samples each of dimension D\n",
    "        - y: A numpy array of shape (num_train,) contain the training labels,\n",
    "          where y[i] is the label of X[i]\n",
    "        - reg: float, regularization strength\n",
    "        Return:\n",
    "        - loss: the loss value between predict value and ground truth\n",
    "        - dW: gradient of W\n",
    "        \"\"\"\n",
    "        \n",
    "        # Initialize loss and dW\n",
    "        loss = 0.0\n",
    "        dW = np.zeros(self.W.shape)\n",
    "        \n",
    "        # Compute the loss and dW\n",
    "        num_train = X.shape[0]\n",
    "        num_classes = self.W.shape[1]\n",
    "        for i in range(num_train):\n",
    "            scores = np.dot(X[i], self.W)\n",
    "            scores -= np.max(scores)\n",
    "            correct_class = y[i]\n",
    "            correct_score = scores[correct_class]\n",
    "            loss_i = -correct_score + np.log(np.sum(np.exp(scores)))\n",
    "            loss += loss_i\n",
    "            for j in range(num_classes):\n",
    "                softmax_output = np.exp(scores[j]) / np.sum(np.exp(scores))\n",
    "                if j == correct_class:\n",
    "                    dW[:,j] += (-1 + softmax_output) * X[i,:]\n",
    "                else:\n",
    "                    dW[:,j] += softmax_output * X[i,:]\n",
    "        \n",
    "        loss /= num_train\n",
    "        loss += 0.5 * reg * np.sum(self.W * self.W)\n",
    "        dW /= num_train\n",
    "        dW += reg * self.W\n",
    "        \n",
    "        return loss, dW\n",
    "    \n",
    "    def loss_vectorized(self, X, y, reg):\n",
    "        \"\"\"\n",
    "        Structured Softmax loss function, vectorized implementation (without loops).\n",
    "        Inputs:\n",
    "        - X: A numpy array of shape (num_train, D) contain the training data\n",
    "          consisting of num_train samples each of dimension D\n",
    "        - y: A numpy array of shape (num_train,) contain the training labels,\n",
    "          where y[i] is the label of X[i]\n",
    "        - reg: float, regularization strength\n",
    "        Return:\n",
    "        - loss: the loss value between predict value and ground truth\n",
    "        - dW: gradient of W\n",
    "        \"\"\"\n",
    "        \n",
    "        # Initialize loss and dW\n",
    "        loss = 0.0\n",
    "        dW = np.zeros(self.W.shape)\n",
    "        \n",
    "        # Compute the loss and dW\n",
    "        num_train = X.shape[0]\n",
    "        num_classes = self.W.shape[1]\n",
    "        \n",
    "        # loss\n",
    "        scores = np.dot(X, self.W)\n",
    "        scores -= np.max(scores, axis=1).reshape(-1, 1)\n",
    "        softmax_output = np.exp(scores) / np.sum(np.exp(scores), axis=1).reshape(-1, 1)\n",
    "        loss = np.sum(-np.log(softmax_output[range(softmax_output.shape[0]), list(y)]))\n",
    "        loss /= num_train\n",
    "        loss += 0.5 * reg * np.sum(self.W * self.W)\n",
    "        \n",
    "        # dW\n",
    "        dS = softmax_output\n",
    "        dS[range(dS.shape[0]), list(y)] += -1\n",
    "        dW = np.dot(X.T, dS)\n",
    "        dW /= num_train\n",
    "        dW += reg * self.W\n",
    "        \n",
    "        return loss, dW\n",
    "    \n",
    "    def train(self, X, y, learning_rate = 1e-3, reg = 1e-5, num_iters = 100, \n",
    "             batch_size = 200, print_flag = False):\n",
    "        \"\"\"\n",
    "        Train Softmax classifier using SGD\n",
    "        Inputs:\n",
    "        - X: A numpy array of shape (num_train, D) contain the training data\n",
    "          consisting of num_train samples each of dimension D\n",
    "        - y: A numpy array of shape (num_train,) contain the training labels,\n",
    "          where y[i] is the label of X[i], y[i] = c, 0 <= c <= C\n",
    "        - learning rate: (float) learning rate for optimization\n",
    "        - reg: (float) regularization strength\n",
    "        - num_iters: (integer) numbers of steps to take when optimization\n",
    "        - batch_size: (integer) number of training examples to use at each step\n",
    "        - print_flag: (boolean) If true, print the progress during optimization\n",
    "        Outputs:\n",
    "        - loss_history: A list containing the loss at each training iteration\n",
    "        \"\"\"\n",
    "        \n",
    "        loss_history = []\n",
    "        num_train = X.shape[0]\n",
    "        dim = X.shape[1]\n",
    "        num_classes = np.max(y) + 1\n",
    "        \n",
    "        # Initialize W\n",
    "        if self.W == None:\n",
    "            self.W = 0.001 * np.random.randn(dim, num_classes)\n",
    "        \n",
    "        # iteration and optimization\n",
    "        for t in range(num_iters):\n",
    "            idx_batch = np.random.choice(num_train, batch_size, replace=True)\n",
    "            X_batch = X[idx_batch]\n",
    "            y_batch = y[idx_batch]\n",
    "            loss, dW = self.loss_vectorized(X_batch, y_batch, reg)\n",
    "            loss_history.append(loss)\n",
    "            self.W += -learning_rate * dW\n",
    "            \n",
    "            if print_flag and t%100 == 0:\n",
    "                print('iteration %d / %d: loss %f' % (t, num_iters, loss))\n",
    "        \n",
    "        return loss_history\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\"\n",
    "        Use the trained weights of Softmax to predict data labels\n",
    "        Inputs:\n",
    "        - X: A numpy array of shape (num_train, D) contain the training data\n",
    "        Outputs:\n",
    "        - y_pred: A numpy array, predicted labels for the data in X\n",
    "        \"\"\"\n",
    "        \n",
    "        y_pred = np.zeros(X.shape[0])\n",
    "        scores = np.dot(X, self.W)\n",
    "        y_pred = np.argmax(scores, axis=1)\n",
    "        \n",
    "        return y_pred        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Gradient Check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def loss_naive1(X, y, W, reg):\n",
    "    \"\"\"\n",
    "    Structured Softmax loss function, naive implementation (with loops).\n",
    "    Inputs:\n",
    "    - X: A numpy array of shape (num_train, D) contain the training data\n",
    "      consisting of num_train samples each of dimension D\n",
    "    - y: A numpy array of shape (num_train,) contain the training labels,\n",
    "      where y[i] is the label of X[i]\n",
    "    - W: A numpy array of shape (D, C) contain the weights\n",
    "    - reg: float, regularization strength\n",
    "    Return:\n",
    "    - loss: the loss value between predict value and ground truth\n",
    "    - dW: gradient of W\n",
    "    \"\"\"\n",
    "        \n",
    "    # Initialize loss and dW\n",
    "    loss = 0.0\n",
    "    dW = np.zeros(W.shape)\n",
    "        \n",
    "    # Compute the loss and dW\n",
    "    num_train = X.shape[0]\n",
    "    num_classes = W.shape[1]\n",
    "    for i in range(num_train):\n",
    "        scores = np.dot(X[i], W)\n",
    "        scores -= np.max(scores)\n",
    "        correct_class = y[i]\n",
    "        correct_score = scores[correct_class]\n",
    "        loss_i = -correct_score + np.log(np.sum(np.exp(scores)))\n",
    "        loss += loss_i\n",
    "        for j in range(num_classes):\n",
    "            softmax_output = np.exp(scores[j]) / np.sum(np.exp(scores))\n",
    "            if j == correct_class:\n",
    "                dW[:,j] += (-1 + softmax_output) * X[i,:]\n",
    "            else:\n",
    "                dW[:,j] += softmax_output * X[i,:]\n",
    "        \n",
    "    loss /= num_train\n",
    "    loss += 0.5 * reg * np.sum(W * W)\n",
    "    dW /= num_train\n",
    "    dW += reg * W\n",
    "        \n",
    "    return loss, dW\n",
    "\n",
    "def loss_vectorized1(X, y, W, reg):\n",
    "    \"\"\"\n",
    "    Structured Softmax loss function, vectorized implementation (without loops).\n",
    "    Inputs:\n",
    "    - X: A numpy array of shape (num_train, D) contain the training data\n",
    "      consisting of num_train samples each of dimension D\n",
    "    - y: A numpy array of shape (num_train,) contain the training labels,\n",
    "      where y[i] is the label of X[i]\n",
    "    - W: A numpy array of shape (D, C) contain the weights\n",
    "    - reg: float, regularization strength\n",
    "    Return:\n",
    "    - loss: the loss value between predict value and ground truth\n",
    "    - dW: gradient of W\n",
    "    \"\"\"\n",
    "        \n",
    "    # Initialize loss and dW\n",
    "    loss = 0.0\n",
    "    dW = np.zeros(W.shape)\n",
    "        \n",
    "    # Compute the loss and dW\n",
    "    num_train = X.shape[0]\n",
    "    num_classes = W.shape[1]\n",
    "        \n",
    "    # loss\n",
    "    scores = np.dot(X, W)\n",
    "    scores -= np.max(scores, axis=1).reshape(-1, 1)\n",
    "    softmax_output = np.exp(scores) / np.sum(np.exp(scores), axis=1).reshape(-1, 1)\n",
    "    loss = np.sum(-np.log(softmax_output[range(softmax_output.shape[0]), list(y)]))\n",
    "    loss /= num_train\n",
    "    loss += 0.5 * reg * np.sum(W * W)\n",
    "        \n",
    "    # dW\n",
    "    dS = softmax_output\n",
    "    dS[range(dS.shape[0]), list(y)] += -1\n",
    "    dW = np.dot(X.T, dS)\n",
    "    dW /= num_train\n",
    "    dW += reg * W\n",
    "        \n",
    "    return loss, dW"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: 1.382074 analytic: 1.382074, relative error: 2.603780e-08\n",
      "numerical: 0.587997 analytic: 0.587997, relative error: 2.764543e-08\n",
      "numerical: 2.466843 analytic: 2.466843, relative error: 8.029571e-09\n",
      "numerical: -1.840196 analytic: -1.840196, relative error: 1.781980e-09\n",
      "numerical: 1.444645 analytic: 1.444645, relative error: 6.200972e-08\n",
      "numerical: -1.381959 analytic: -1.381959, relative error: 1.643225e-08\n",
      "numerical: 1.122692 analytic: 1.122692, relative error: 1.600617e-08\n",
      "numerical: 1.249459 analytic: 1.249459, relative error: 2.936177e-09\n",
      "numerical: 1.556929 analytic: 1.556929, relative error: 1.452262e-08\n",
      "numerical: 1.976238 analytic: 1.976238, relative error: 1.619212e-08\n",
      "numerical: 2.308430 analytic: 2.308430, relative error: 7.769452e-10\n",
      "numerical: -2.698441 analytic: -2.698440, relative error: 2.672068e-08\n",
      "numerical: 1.991475 analytic: 1.991475, relative error: 3.035301e-08\n",
      "numerical: -1.891048 analytic: -1.891048, relative error: 1.407403e-08\n",
      "numerical: 1.409085 analytic: 1.409085, relative error: 1.916174e-08\n",
      "numerical: 1.688600 analytic: 1.688600, relative error: 6.298778e-10\n",
      "numerical: -0.140043 analytic: -0.140043, relative error: 7.654000e-08\n",
      "numerical: -0.563577 analytic: -0.563577, relative error: 5.109196e-08\n",
      "numerical: 0.224879 analytic: 0.224879, relative error: 1.218421e-07\n",
      "numerical: -5.497099 analytic: -5.497099, relative error: 1.992705e-08\n"
     ]
    }
   ],
   "source": [
    "from gradient_check import grad_check_sparse\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001\n",
    "\n",
    "# Without regularization\n",
    "loss, dW = loss_naive1(X_dev, y_dev, W, 0)\n",
    "f = lambda W: loss_naive1(X_dev, y_dev, W, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, dW)\n",
    "\n",
    "# With regularization\n",
    "loss, dW = loss_naive1(X_dev, y_dev, W, 5e1)\n",
    "f = lambda W: loss_naive1(X_dev, y_dev, W, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, dW)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### loss_naive vs. loss_vectorized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 2.365604e+00 computed in 0.245464 seconds.\n",
      "Vectorized loss: 2.365604e+00 computed in 0.008947 seconds.\n",
      "Difference of loss: 0.000000\n",
      "Difference of dW: 0.000000\n"
     ]
    }
   ],
   "source": [
    "t_st = time.time()\n",
    "loss_naive, dW_naive = loss_naive1(X_dev, y_dev, W, 0.00005)\n",
    "t_ed = time.time()\n",
    "print('Naive loss: %e computed in %f seconds.' % (loss_naive, t_ed - t_st))\n",
    "t_st = time.time()\n",
    "loss_vectorized, dW_vectorized = loss_vectorized1(X_dev, y_dev, W, 0.00005)\n",
    "t_ed = time.time()\n",
    "print('Vectorized loss: %e computed in %f seconds.' % (loss_vectorized, t_ed - t_st))\n",
    "\n",
    "diff_loss = loss_naive - loss_vectorized\n",
    "diff_dW = np.linalg.norm(dW_naive - dW_vectorized, ord='fro')\n",
    "print('Difference of loss: %f' % diff_loss)\n",
    "print('Difference of dW: %f' % diff_dW)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Stochastic Gradient Descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 389.013148\n",
      "iteration 100 / 1500: loss 235.704700\n",
      "iteration 200 / 1500: loss 142.948192\n",
      "iteration 300 / 1500: loss 87.236112\n",
      "iteration 400 / 1500: loss 53.494956\n",
      "iteration 500 / 1500: loss 33.153764\n",
      "iteration 600 / 1500: loss 20.907861\n",
      "iteration 700 / 1500: loss 13.442687\n",
      "iteration 800 / 1500: loss 8.929345\n",
      "iteration 900 / 1500: loss 6.238832\n",
      "iteration 1000 / 1500: loss 4.559590\n",
      "iteration 1100 / 1500: loss 3.501153\n",
      "iteration 1200 / 1500: loss 2.924789\n",
      "iteration 1300 / 1500: loss 2.552109\n",
      "iteration 1400 / 1500: loss 2.370926\n"
     ]
    }
   ],
   "source": [
    "softmax = Softmax()\n",
    "loss_history = softmax.train(X_train, y_train, learning_rate = 1e-7, reg = 2.5e4, num_iters = 1500, \n",
    "             batch_size = 200, print_flag = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jkNdRAABAmqCgecznM106oVwrtjOCBgAAYihoaWDhpHLt6DjGemgAAEASBS0tLJhYLkla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\n/ZmrtHhunf7tuW161/0vsZc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e50kFMxtrZs+Z2UYz22Bmn44fLzezp81sa/yxbMhn7o2f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\nC2BhNIaxRScAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd32e9746a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss_history\n",
    "plt.plot(loss_history)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training correct 17023/49000: The accuracy is 0.347408\n",
      "Test correct 359/1000: The accuracy is 0.359000\n"
     ]
    }
   ],
   "source": [
    "# Use softmax classifier to predict\n",
    "# Training set\n",
    "y_pred = softmax.predict(X_train)\n",
    "num_correct = np.sum(y_pred == y_train)\n",
    "accuracy = np.mean(y_pred == y_train)\n",
    "print('Training correct %d/%d: The accuracy is %f' % (num_correct, X_train.shape[0], accuracy))\n",
    "\n",
    "# Test set\n",
    "y_pred = softmax.predict(X_test)\n",
    "num_correct = np.sum(y_pred == y_test)\n",
    "accuracy = np.mean(y_pred == y_test)\n",
    "print('Test correct %d/%d: The accuracy is %f' % (num_correct, X_test.shape[0], accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Validation and Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr: 1.400000e-07 reg: 8.000000e+03 train accuracy: 0.376388 val accuracy: 0.381000\n",
      "lr: 1.400000e-07 reg: 9.000000e+03 train accuracy: 0.378061 val accuracy: 0.393000\n",
      "lr: 1.400000e-07 reg: 1.000000e+04 train accuracy: 0.375061 val accuracy: 0.394000\n",
      "lr: 1.400000e-07 reg: 1.100000e+04 train accuracy: 0.370918 val accuracy: 0.389000\n",
      "lr: 1.400000e-07 reg: 1.800000e+04 train accuracy: 0.361857 val accuracy: 0.378000\n",
      "lr: 1.400000e-07 reg: 1.900000e+04 train accuracy: 0.354327 val accuracy: 0.373000\n",
      "lr: 1.400000e-07 reg: 2.000000e+04 train accuracy: 0.357531 val accuracy: 0.370000\n",
      "lr: 1.400000e-07 reg: 2.100000e+04 train accuracy: 0.351837 val accuracy: 0.374000\n",
      "lr: 1.500000e-07 reg: 8.000000e+03 train accuracy: 0.380429 val accuracy: 0.387000\n",
      "lr: 1.500000e-07 reg: 9.000000e+03 train accuracy: 0.375959 val accuracy: 0.393000\n",
      "lr: 1.500000e-07 reg: 1.000000e+04 train accuracy: 0.373857 val accuracy: 0.397000\n",
      "lr: 1.500000e-07 reg: 1.100000e+04 train accuracy: 0.371918 val accuracy: 0.386000\n",
      "lr: 1.500000e-07 reg: 1.800000e+04 train accuracy: 0.359735 val accuracy: 0.379000\n",
      "lr: 1.500000e-07 reg: 1.900000e+04 train accuracy: 0.359796 val accuracy: 0.373000\n",
      "lr: 1.500000e-07 reg: 2.000000e+04 train accuracy: 0.352041 val accuracy: 0.365000\n",
      "lr: 1.500000e-07 reg: 2.100000e+04 train accuracy: 0.356531 val accuracy: 0.372000\n",
      "lr: 1.600000e-07 reg: 8.000000e+03 train accuracy: 0.378265 val accuracy: 0.394000\n",
      "lr: 1.600000e-07 reg: 9.000000e+03 train accuracy: 0.377980 val accuracy: 0.391000\n",
      "lr: 1.600000e-07 reg: 1.000000e+04 train accuracy: 0.371429 val accuracy: 0.389000\n",
      "lr: 1.600000e-07 reg: 1.100000e+04 train accuracy: 0.374224 val accuracy: 0.391000\n",
      "lr: 1.600000e-07 reg: 1.800000e+04 train accuracy: 0.360796 val accuracy: 0.386000\n",
      "lr: 1.600000e-07 reg: 1.900000e+04 train accuracy: 0.355592 val accuracy: 0.371000\n",
      "lr: 1.600000e-07 reg: 2.000000e+04 train accuracy: 0.356122 val accuracy: 0.368000\n",
      "lr: 1.600000e-07 reg: 2.100000e+04 train accuracy: 0.354143 val accuracy: 0.367000\n",
      "Best validation accuracy during cross-validation:\n",
      "lr = 1.500000e-07, reg = 1.000000e+04, best_val = 0.397000\n"
     ]
    }
   ],
   "source": [
    "learning_rates = [1.4e-7, 1.5e-7, 1.6e-7]\n",
    "regularization_strengths = [8000.0, 9000.0, 10000.0, 11000.0, 18000.0, 19000.0, 20000.0, 21000.0]\n",
    "\n",
    "results = {}\n",
    "best_lr = None\n",
    "best_reg = None\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_softmax = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "for lr in learning_rates:\n",
    "    for reg in regularization_strengths:\n",
    "        softmax = Softmax()\n",
    "        loss_history = softmax.train(X_train, y_train, learning_rate = lr, reg = reg, num_iters = 3000)\n",
    "        y_train_pred = softmax.predict(X_train)\n",
    "        accuracy_train = np.mean(y_train_pred == y_train)\n",
    "        y_val_pred = softmax.predict(X_val)\n",
    "        accuracy_val = np.mean(y_val_pred == y_val)\n",
    "        results[(lr, reg)] = accuracy_train, accuracy_val\n",
    "        if accuracy_val > best_val:\n",
    "            best_lr = lr\n",
    "            best_reg = reg\n",
    "            best_val = accuracy_val\n",
    "            best_softmax = softmax\n",
    "        print('lr: %e reg: %e train accuracy: %f val accuracy: %f' %\n",
    "              (lr, reg, results[(lr, reg)][0], results[(lr, reg)][1]))\n",
    "print('Best validation accuracy during cross-validation:\\nlr = %e, reg = %e, best_val = %f' %\n",
    "      (best_lr, best_reg, best_val)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the cross-validation result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f9WZ2BrAOGNiDY8wGXiP+1ld724BrgLN7sD8RERFJs1xKZsIKU9Pz7WCw0f8C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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd32d101320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "maker_size = 100\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "# training accuracy\n",
    "plt.subplot(2, 1, 1)\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.scatter(x_scatter, y_scatter, maker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "\n",
    "# validation accuracy\n",
    "plt.subplot(2, 1, 2)\n",
    "colors = [results[x][1] for x in results]\n",
    "plt.scatter(x_scatter, y_scatter, maker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use the best softmax to test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test correct 379/1000: The accuracy is 0.379000\n"
     ]
    }
   ],
   "source": [
    "y_pred = best_softmax.predict(X_test)\n",
    "num_correct = np.sum(y_pred == y_test)\n",
    "accuracy = np.mean(y_pred == y_test)\n",
    "print('Test correct %d/%d: The accuracy is %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the weights for each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y9SW4n9Mhpi7jrFmBKDERaLN+HXtYr9MjYnnR4b4XEeSodaJ/xHPGvU0Gn6gs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3Pr2OykzHy2VHb9rJLLYjHk+EaTdn5ZZWQXkdATvn+mUk/4t9jvTlVm9e4B941CnPE09jkmc05f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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd32cfab4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "W = best_softmax.W[:-1,:]    # delete the bias\n",
    "W = np.reshape(W, (32, 32, 3, 10))\n",
    "W_max, W_min = np.max(W), np.min(W)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "plt.figure(figsize=(10,5))\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i+1)\n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    imgW = 255.0 * (W[:,:,:,i] - W_min) / (W_max - W_min)\n",
    "    plt.imshow(imgW.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
